An Enhanced Fast – High Utility Item set Mining Method for Large Datasets

Abstract

High Utility Itemset mining is considered one of the critical and challenging problems in data mining. The existing mining framework is limited to analyzing occurrence counts of items in the Database. However, this framework applies a single minimum utility threshold value that fails to consider different item characteristics. Recent methods of association mining focused on finding the high utility itemsets instead of frequent itemsets generations. Some utility-based mining methods that is Faster High Utility Itemset Mining (FHM), High Utility Itemset Miner (HUI-Miner), Direct Discovery of High Utility Patterns (D2HUP), Utility Pattern Growth (UP Growth & UP Growth+) are studied for the generation of high utility itemsets generations. Existing HUI mining methods are effectively generating HUIs. However, developing a faster and memory-efficient HUI mining method is required. For this purpose, this work develops an Enhanced Fast - High Utility Itemset Mining (EF-HUIM) method for the faster generation of high utility itemsets and respective association rules.